This project aims at exploring how various metrics/proxies can give insights on the vertical distribution of the phytoplankton biomass in the water column using the data measured during the Green Edge oceanographic cruise conducted in the Baffin Bay in 2016. Four main indicators were considered to get insights about the phytoplankton biomass in the water column:
Most of the data is presented as a function of open water days (OWD) that were calculated in Randelhoff et al. (2019). The first graph shows the OWD of each station.
After exploring the CTD data, I found out that there were some unwanted peaks in both the fluorescence and transmittance data. Therefore, I have used a median moving median window (\(n = 25\)) to smooth the data (both transmittance and fluorescence).
During the meeting we had on 2021-01-22, it was questioned why I was averaging data by owd and depth before doing the interpolation. This is simply because there may be many observations for a specific pair of owd and depth. For example, it can be seen here that there are 9 values (i.e. 9 stations) that have an owd = 2 and depth = 1.980.
| owd | depth_m | N |
|---|---|---|
| 2 | 1.980 | 9 |
| 2 | 2.178 | 9 |
| 2 | 2.376 | 9 |
| 2 | 2.574 | 9 |
| 2 | 2.772 | 9 |
| 2 | 2.970 | 9 |
Here are 9 stations in the CTD data with owd = 2 and depth = 1.980.
Hence, before performing the interpolation, data has to be averaged by OWD and depth.
This section shows the vertical distribution of phytoplankton based on fluorescence.
Using boxplots can be one interesting alternative way to present the data. For example, I have divided the CTD fluorescence data presented in the above graphic into ice-free/ice-covered and above/below the 0.1 isolume.
Note that because the MVP is measuring continually, there are no stations associated with each measurement. This is why that the MVP data is not presented as a function of OWD.
These graphs were made using the pigments data from the rosette. Pigments were summed into two groups:
The increase of cp during the lighted portion of the day is usually explained by the accumulation of intracellular carbon concentration associated with photosynthetic processes (Kheireddine and Antoine 2014).
The CP visualization based on the CTD can be seen above. There is a clear relationship between fluorescence and CP as seen below.
The next graph shows the same data but is divided differently. The best relationships can be seen for the observations below the 0.1 isolume and located in open water. I think the bottom left panel is the most interesting where we can see the L shape.
Finally, the same data by transect.
The next graphic shows CP vertical profiles for some of the deepest stations below 500 meters. Any ideas why there is almost always a bump in the data in the very last portion of the profiles?
The next graphs show the longitudinal variability of CP at the deepest locations within each transect. The left column shows how the MVP was moving in the water column whereas the right column shows how CP varied along the transect. For example, we can see that the MVP followed the 300 meters line at transect 100.
These graphs show bbp at six different wavelengths measured by the hydroscat.
The next graphs explore the relationships between bbp and chlorophyll-a fluorescence from the Hydroscat device.
The same data divided differently.
Scatterplot between bbp measured at 532 nm and 700 nm.
The same data presented differently.
Maybe we could use absorption information to get some additional insights.
This graph shows the vertical profiles of phytoplankton absorption at 440 nm. It seems that open water stations have subsurface maximum more apparent than the profiles of the ice-covered stations.
By looking at the spectral profiles, we can also notice that there are indeed differences in the water column.
Finally, I have averaged the spectral profiles in four categories. I think these can be seen as end-members spectra.
With all the data.
By groups.
We can see that there is an increase in the ratio after owd = 0. I am not sure to understand why the ratio is also higher at depth when owd < 0. Is there a shift when the ice is melting?
The next graphs show the relationships between POC and CP.
Note that I have used chla fluorescence from the Hydroscat which is given as raw counts. If we decide to go further with this data, I guess we could convert it into actual biomass/stock quantities.
The ratio bbp(532)/cp(660) could be considered as a proxy of particle size and composition, increasing when small or inorganic particles become relatively more abundant than large or organic particles (Xing et al. 2014).
So, based on the next graphs, larger particles are more abundant at depth than at the surface. However, I do not see trends in the data.
This index was calculated as follow:
\[ \frac{\log(\frac{\text{bbp}(532)}{\text{bbp}(700)})}{0.274} \]
This graph show how many observations (different than 0) there are for each particle class size measured by the UVP.
In this section, we are exploring the size of the particles in the water column using the data from the UVP. Based on a suggestion from Marcel, we categorized the size of particle into three classes:
The next table shows the different UPV particle size ranges that were classified into the three end-member classes. Particle biovolume and concentration of the different UVP class sizes were summed up to calculate the total values within each of the three end-member classes.
| particle_size_range | particle_size_class |
|---|---|
| 102-128 µm | particle_class_small |
| 128-161 µm | particle_class_small |
| 161-203 µm | particle_class_small |
| 203-256 µm | particle_class_small |
| 256-323 µm | particle_class_small |
| 323-406 µm | particle_class_medium |
| 406-512 µm | particle_class_medium |
| 512-645 µm | particle_class_medium |
| 645-813 µm | particle_class_medium |
| 0.813-1.02 mm | particle_class_medium |
| 1.02-1.29 mm | particle_class_large |
| 1.29-1.63 mm | particle_class_large |
| 1.63-2.05 mm | particle_class_large |
| 2.05-2.58 mm | particle_class_large |
| 2.58-3.25 mm | particle_class_large |
| 3.25-4.1 mm | particle_class_large |
| 4.1-5.16 mm | particle_class_large |
| 5.16-6.5 mm | particle_class_large |
| 6.5-8.19 mm | particle_class_large |
| 8.19-10.3 mm | particle_class_large |
| 10.3-13 mm | particle_class_large |
| 13-16.4 mm | particle_class_large |
| 16.4-20.6 mm | particle_class_large |
| 20.6-26 mm | particle_class_large |
This simple boxplot shows how particle biovolume and concentration vary as a function of the three-class sizes. The particle concentration decreases with increasing particle size whereas the biovolume is rather stable.
Here, I averaged the particle concentration within the first 20 meters of the water column and plotted the results as a function of OWD.
ODW = 10 and the decreases.ODW = 0 and the decreases.This is interesting because this suggests that there are some kind of phenology/timing going on and a temporal shift between the different particle size classes (at least at the surface).
Here, I have divided the water column into 5 bins and look at the relationships between the concentration and the biovolume of particle.
In this analysis I used a technique proposed in Vandermeulen et al. (2020).
The location of the AVW effectively represents the balance point around which reflectance data is evenly distributed, or more informally, where a Rrs(λ) spectrum would be perfectly balanced on the tip of a pin if each individual channel held a physical weight proportional to its intensity.
This graph shows all the phytoplankton absorption spectra colored by their calculated AVW value.
Using the above data, I calculated the average AVW by depth. We can see that there is a shift from blue to green along the water column, suggesting that there is a change in the spectral shape of phytoplankton.
If we only keep observations above the isolume, the relationship is even clearer.
Rosette pigments were summed as follow:
Photoprotection: Zeaxanthin, Diatoxanthin, Diadinoxanthin, Antheraxanthin, Violaxanthin
Photosynthetic: Sum 19HF-like, Sum 19BF-like, Peridinin, Fucoxanthin, Chlorophyll c3, Chlorophyll c1+c2+MgDVP, Chlorophyll b, Lutein
We can see that the relative proportion of small particles increase with increasing OWD.
Change in the phytoplankton community?
Can we use pigments to get insights on how the phtoplankton community change over time?
When bbp/cp increases, small particles become relatively more abundant than large particles.
At high bbp/cp (i.e. higher contribution of small particle to the total pool):
In this figure, the total particle count is defined as the sum of these three classes:
Maybe use all UVP data and not not only the 3 defined classes.
Using all the data (not only <= 100 meters).
I have calculated the number of open water days using the same data an Achim. I have used a threshold of 15% at least for 3 consecutive days.
We can see that there are not many observations with negative OWD because the MVP was deployed in open water.
Co-variability of subsurface maximums for bbp, cp and chla. At each OWD, the depth of the maximum value (ex.: chla) was extracted. Note that the depths at which each variable is maximum was calculated on raw data and not the interpolated data from Fig. 02. The points are the raw data and the lines represent values fitted using loess.
It was previously that chla/cp was a good indicator of phytoplankton photoacclimation (Ek) and physiology (Fv/Fm).
Very weak relations with either Ek (r = -0.16) or Fv/Fm (r = 0.26).
Determine if CP correction (baseline shift) should be performed or not.
Check if there is a relation between particle size ratio and bbp/cp ratio (see figure 9).
Add figure on correlation between CP and POC (see Fig. 4 in Behrenfeld 2006).
Make a graph with POC vs other bio-optical variable (chla, cp, bbp).